Akash Saravanan is an Applied Scientist with 9 years of experience, currently focused on applied NLP and LLM work at Qualtrics, specializing in fine-tuning, prompt engineering, and alignment/RLHF. He combines research-grade expertise in computational creativity and reinforcement learning—his MS thesis produced a system for balancing competitive game characters—with practical deployment experience building production ML systems. At Georgian he led GenAI bootcamps and maintained widely used open-source toolkits (including a 500+ star Multimodal Toolkit), delivering numerous RAG and multimodal solutions into production. His background spans computer vision, graph ML, privacy-aware ML, and back-end engineering, highlighted by contributions to a math problem generator and production-ready NLP pipelines. Based in Vancouver, he brings a blend of academic publications, hands-on model engineering, and program-level leadership that accelerates adoption of GenAI across companies.
9 years of coding experience
6 years of employment as a software developer
Master of Science Computing Science, Master of Science Computing Science at University of Alberta
High School Computer Science, High School Computer Science at Asan Memorial Senior Secondary School
Bachelor of Engineering - BE Computer Science and Engineering, Bachelor of Engineering - BE Computer Science and Engineering at Sri Venkateswara College of Engineering
English, Tamil
Github Skills (10)
mathematics10
generator10
math10
python10
data-structure9
algorithm9
data-structures9
algorithms9
maths8
ed255198
Programming languages (8)
DockerfileJavaC++JavaScriptHTMLJupyter NotebookRich Text FormatPython
A math problem generator, created for the purpose of giving self-studying students and teaching organizations the means to easily get access to high-quality, generated math problems to suit their needs.
Role in this project:
Back-end Developer
Contributions:5 commits, 4 PRs, 10 comments in 1 day
Contributions summary:Akash primarily contributed to adding new mathematical problem types to the math generator. Their work involved implementing functions for power rule differentiation and finding the intersection of two lines, enhancing the generator's capabilities. These additions required writing Python code that defined mathematical formulas, handling potential edge cases like parallel lines, and generating corresponding problem and solution sets. The user also fixed a merge conflict within the codebase, showing attention to code maintenance.
Contributions:6 commits, 6 pushes, 1 branch in 7 months
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